100 Days of ML — Day 2 — How Much Math Is Necessary Or How I Accidentally Got Into Deep Learning

As mentioned yesterday, I really wanted to be successful at podcasting, YouTube, Instagram, Twitch, maybe SnapChat, places where it seems like a lot of very talented people are making very large sums of money.

I had overlooked how important that talent piece was, so, again, as I said yesterday, I hedged my bets with AI. One site tantalized me with the promise that all I needed was the bare minimum of stats, calculus, and linear algebra. I hadn’t done that since high school and I wasn’t awesome at them then, but I figured a quick brush-up and I’d be Good Will Hunting some Machine Learning in no time.

The first section, build a neural network, was a bit of a struggle. The intuition was easy, but I had definitely never bothered to learn classes in programming. I’m more of a “use what it takes to get the problem solved” kinda guy. I basically duct tape code together and hope for the best.

The next section, build a CNN (convolutional neural network), was a tad harder, but, thanks to my sink or swim strategy, I actually managed to get through it faster. The intuition really spoke to me. The computer scans over pictures to build a map of small features, each layer of linear algebra mapping larger and larger features.

That was encouraging, but ultimately dreadful as the one thing I wanted to was text based data. The task was to automate Simpsons script writing, which was great for my goal of automating comedy. I was terrible at the intuition and the writing of it.

I bring this up today, because I watch a ton of Siraj and his video on “Sports Betting With Reinforcement Learning” was just slightly over my head.

Ladies and gentlemen, AI is going to disrupt a lot very quickly (a series of articles I’ll get into later). In this new society, AI engineers and data scientists will always have work. In this new economy, as industries emerge and die constantly, you’ll have to learn something new every day to stay competitive. I urge you to take more than the bare minimum of stats, calculus, and linear algebra.

There are a number of resources on YouTube and Khan Academy as well as many online course sites.

In the meantime, I’m inspired to continue my course to take anyone from 0 to Deep Learning.

I did it. And that means anyone can.

Jimmy Murray is a Florida based comedian who studied Marketing and Film before finding himself homeless. Resourceful, he taught himself coding, which led to a ton of opportunities in many field, the most recent of which is coding away his podcast editing. His entrepreneurial skills and love of automation have led to a sheer love of all things related to AI.